Description of the IRI Experimental Seasonal Typhoon Activity Forecasts

Description of the IRI Experimental Seasonal Typhoon Activity

ForecastsSuzana J. Camargo,

Anthony G. Barnston and Stephen E.Zebiak

October 25-26, 2003, Taipei, Taiwan

International Workshop on Monthly-to-Seasonal Climate Prediction

Introduction

• 2003 – IRI experimental seasonal forecasts on typhoon activity. http://iri.columbia.edu/forecast/typhoon

• Probabilistic forecasts first released in April 2003.

• Forecasts updated monthly: April, May, June and July 2003.

• Forecasts for the peak typhoon season: July to October (JASO).



How are the forecasts produced?

1. Sea Surface Temperature forecasts produced.2. Atmopheric Model forced by sea surface

temperature forecasts.3. Tropical Cyclone-like structures detected and

tracked.4. Statistical corrections of the tropical cyclone

activity based on the model climatology.5. Probabilistic forecasts of tropical cyclone activity.6. IRI Seasonal Typhoon Outlooks released



Sea surface temperature forecasts

IRI sea surface temperature (SST) forecasts• Anomalous SST forecast:

Dynamical SST forecast – Pacific Ocean Statistical SST forecasts– Atlantic and Indian

Oceans

• Persisted SST (shorter lead time)



SST forecast ASO 2003



Persisted SST



Atmospheric General Circulation Model forced with IRI SST forecasts

ECHAM4.5 Atmospheric General Circulation Model (AGCM) forced with IRI SST forecasts.

– For each SST scenario (forecast and persisted) 24 ensemble members with different initial conditions for the atmosphere are produced.



Sea Surface Temperature Forecasts(Cont.)

• Forecast SST – 6 months lead time.Example: July forecast – integrated until

January, using observed data from June.

• Persisted SST – 4 months lead time.Example: July forecast – integrated until

November, using observed data from June.



Typical AGCM Tropical Cyclone



Typical AGCM Tropical Cyclone 2

Vorticity Wind Speed

Precipitation Humidity



Tropical Cyclones in AGCMs

• Numerous studies showed that AGCMs can create model tropical cyclones with strong similarities to observed tropical cyclones:Cyclonic vorticity, convergence and high moisture

content at lower levels.Heavy precipitation and local maximum of surface

winds.Strong upward motion, positive local temperature

anomaly throughout the troposphere.Anti-cyclonic vorticity and divergency at upper levels.



Tropical Cyclones in AGCMs

• Development in areas of SSTs above 26oC.• Vertical structure similar to observed tropical

cyclones composites.

• Model tropical cyclones in LOW resolution AGCMs have deficiencies:– Lack the presence of an eye, eye-wall and rainbands.– Horizontal extension larger than observed tropical

cyclones.



Detecting and Tracking Tropical Cyclone-like structures

• Using the output of the AGCM integrations, tropical cyclone-like structures are detected and tracked.

• Variables used in the detection and tracking algorithms:– Vorticity, sea level pressure, wind speed,

temperature.



Detecting of Tropical Cyclone-like structures in AGCMS.

• The detection algorithm requires that – The 850hPa relative vorticity,– the surface wind speed– the local temperature anomaly in different pressure

levels throughout the troposphere,– and the sea level pressure

simultaneously satisfy a set of threshold criteria which are defined using the model statistics.

• All these criteria must be satisfied simultaneously for at least 1.5 days.



Tracking Tropical Cyclone-like structures in AGCMs

• The tropical-cyclone like structures are then tracked using the low-level vorticity using a relaxed threshold criterium.

• The vorticity centroid is defined as the tropical cyclone center.















Statistical Corrections of model tropical cyclone activity

• Model distribution of e.g. Number of Tropical Cyclones (NTC) is slightly different from the observed distribution due to model biases.

• Correction of the model climatological distribution based on the percentiles of the observed climatological distribution.





Probabilistic Forecasts of Tropical Cyclone Activity

• Raw probabilistic forecasts obtained based on the distribution of the different ensemble members in the different terciles of tropical cyclone activity variables, e.g. number of tropical cyclones.



0/55/45 0/10/90



IRI Seasonal Typhoon Activity Outlooks

• Raw probabilities subjectively damped by forecasters taking into account possible errors in the SST forecasts and the fact that the model does not have perfect skill.

• Statement with forecast issued in the IRI web page, discussing the relation of the tropical cyclone activity forecast with the SST forecast.

The IRI Typhoon Activity Forecasts

July 2003 IRI Typhoon Activity Forecast approximately 45%) that the number of named tropical cyclones in the western North Pacific during the 2003 peak season (July to October) will be in the below normal category, and a 35% that the number of cyclones will be in the normal category. The normal category is defined as between 17 and 20 named tropical cyclones. These probabilities are slightly greater than the long-term average probability of 33%. The accumulated cyclone energy (ACE*) index during these months also has an enhanced (approximately 45%) probability of being in the below normal range. Furthermore, a slight shift in the average longitude (westward) and latitude (southward) of tropical cyclone tracks is predicted. This forecast is consistent with the near-neutral conditions in the tropical Pacific sea surface temperatures, as shown in our SST forecast.

Background Information

The mean number of observed western Pacific named tropical cyclones (1971-2002) in the peak season is 18.4 with a standard deviation of 3.4. The lowest number of tropical cyclones in the peak season during this historical period was 13 and the maximum was 28. If the peak season climatological median ACE in the period 1971-2002 is defined as 100%, the normal range varies between 89% and 118%. The standard deviation of the ACE index is 40%, but in extreme years the index can exceed 200% or be less than 50%. The historical variability in the ACE index is proportionately larger than the variability of the number of named tropical cyclones, as it takes into account not only the number of tropical cyclones but also their intensity and duration.

This outlook was produced by tracking western North Pacific typhoon-like systems in one of our operational atmospheric general circulation models (AGCMs), ECHAM4.5, forced with IRI's predicted sea surface temperatures . While low-resolution (approximately 2.8 degrees longitude and latitude) AGCMs are not adequate for forecasts of individual typhoons, they can have significant skill in predicting the amount and location of tropical cyclone activity over specific basins, as is the case for the ECHAM4.5 over the western North Pacific. Model tropical cyclones are weaker and larger than observed, but have an identifiable signature with many observed tropical cyclone characteristics. The model skill is due to the variability of the tropical cyclone activity being mainly determined by large-scale variables that affect that activity, such as sea surface temperatures and vertical wind shear, which can be predicted using AGCMs. The spatial and temporal distributions of these model tropical cyclones in the western North Pacific are similar to those of observed tropical cyclones in the region. The average tracks and genesis locations of both model and observed western North Pacific tropical cyclones are also strongly influenced by ENSO. These locational variables have an important impact on the percentage of tropical cyclones which make landfall. In El Niño years there usually is an east-southeast shift in the average track and genesis position, while in La Niña years a west-northwest shift usually occurs.



Skill Scores Number of Tropical Cyclones (NTC)





ACE Index

• ACE : Accumulated Cyclone Energy• Definition: sum of the squares of the estimated 6-

hourly maximum sustained wind speed for all periods in which the observed tropical cyclones had either tropical storm or higher intensity.

• MODELS: all periods with tropical cyclone activity are considered in the model ACE index.





Skill Scores Accumulated Cyclone Energy (ACE)







r =0.54

r = 0.35





Next steps• Improve presentation of the forecasts, by adding

graphical information of the forecasts.• Develop method to translate the “raw”

probabilities into “real” probabilities by an objective method.

• One of IRI current main efforts is to improve the IRI SST forecasts, that will consequently improve the typhoon activity forecasts.

• Possible addition of more AGCMs in the typhoon forecasts, so that multi-model techniques can be used.



Next Steps (Cont.)

• Add new information on the forecasts that could be of interest, such as the season peak and landfall risk.

• Possible use of AGCMs with higher numerical resolution.

Documents

Description of the IRI Experimental Seasonal Typhoon Activity Forecasts